Multi-Channel Fetal ECG Denoising With Deep Convolutional Neural Networks

Eleni Fotiadou (Corresponding author), Rik Vullings

Research output: Contribution to journalArticleAcademicpeer-review

18 Citations (Scopus)


Non-invasive fetal electrocardiography represents a valuable alternative continuous fetal monitoring method that has recently received considerable attention in assessing fetal health. However, the non-invasive fetal electrocardiogram (ECG) is typically severely contaminated by a considerable amount of various noise sources, rendering fetal ECG denoising a very challenging task. This work employs a deep learning approach for removing the residual noise from multi-channel fetal ECG after the maternal ECG has been suppressed. We propose a deep convolutional encoder-decoder network with symmetric skip-layer connections, learning end-to-end mappings from noise-corrupted fetal ECG signals to clean ones. Experiments on simulated data show an average signal-to-noise ratio (SNR) improvement of 9.5 dB for fetal ECG signals with input SNR ranging between −20 and 20 dB. The method is additionally evaluated on a large set of real signals, demonstrating that it can provide significant quality improvement of the noisy fetal ECG signals. We further show that employment of multi-channel signal information by the network provides superior and more reliable performance as opposed to its single-channel network counterpart. The presented method is able to preserve beat-to-beat morphological variations and does not require any prior information on the power spectra of the noise or the pulse location.
Original languageEnglish
Article number508
Number of pages13
JournalFrontiers in Pediatrics
Publication statusPublished - 26 Aug 2020


  • Fetal ECG denoising
  • Convolutional Neural Network
  • fetal electrocardiography
  • deep learning


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